Accountability Layers: Explaining Complex System Failures by Parts

نویسندگان

چکیده

With the rise of AI used for critical decision-making, many important predictions are made by complex and opaque algorithms. The aim eXplainable Artificial Intelligence (XAI) is to make these decision-making algorithms more transparent trustworthy. This often done constructing an ``explainable model'' a single modality or subsystem. However, this approach fails systems that out multiple parts. In paper, I discuss how explain system failures. represent machine as hierarchical model introspective sub-systems working together towards common goal. subsystems communicate in symbolic language. work creates set explanatory accountability layers trustworthy AI.

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ژورنال

عنوان ژورنال: Proceedings of the ... AAAI Conference on Artificial Intelligence

سال: 2023

ISSN: ['2159-5399', '2374-3468']

DOI: https://doi.org/10.1609/aaai.v37i13.26806